Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.
Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.
复制标题
估计多电极神经记录的流形的维数。
DOI：
10.1371/journal.pcbi.1008591
复制
发表时间：
2021-11
影响因子：
4.3
通讯作者：
Perreault EJ
中科院分区：
文献类型：
--
作者：
Altan E;Solla SA;Miller LE;Perreault EJ
人们普遍认为，大脑特定区域的神经元数量远远超过了该区域控制的任何特定功能所需的神经元数量。例如，人类大脑的运动区域包含数千万个神经元，这些神经元控制数十个或至多数百个肌肉的激活。这种巨大的冗余意味着许多神经元的协变，这将种群活动限制在所有可能的神经活动模式空间内的低维流形上。为了从概念上理解流形内神经活动的复杂性，估计其维数是有用的，其量化了描述所观察到的群体活动而没有显著信息损失所需的自由度的数量。虽然有许多算法用于维度估计，但我们不知道哪些算法非常适合分析神经活动。本研究的目的是评估几个代表性的算法估计线性和非线性嵌入数据的维数的有效性。我们生成了具有已知内在维度的合成神经记录，并使用它们来测试算法的准确性和鲁棒性。我们模拟了一些与实验数据相关的重要挑战，通过添加噪声，改变高维记录中低维流形的嵌入性质，改变流形的维度，并限制可用数据的数量。我们证明了线性算法高估了非线性无噪声数据的维数。在高噪声的情况下，大多数算法高估了维数。因此，我们开发了一种基于深度学习的去噪算法，即“联合自动编码器”，它显著改善了后续的维度估计。重要的是，我们发现，当内在维度很高（超过20）或用于估计的数据量很低时，所有算法都失败了。基于我们观察到的挑战，我们制定了一个管道来估计实验神经数据的维度。几十年来，我们可以同时记录的神经元数量呈指数级增长;今天，这个数字是成千上万的神经元。然而，个体的发射率是高度冗余的。从冗余数据中识别重要特征的一种方法是估计神经记录的维数，其表示在没有显著信息损失的情况下描述数据所需的自由度的数量。更好地理解维度也可能揭示神经回路中的计算机制。执行复杂计算的电路可能比执行简单计算的电路更高维。通常，研究使用几种可用的方法之一来量化神经维度，反映出对哪种方法最适合神经数据缺乏共识。在这项工作中，我们使用了几种方法来研究模拟神经数据的准确性，这些数据具有模仿实际神经记录的特性。基于这些结果，我们设计了一个分析管道来估计神经记录的维度。我们的工作将使科学家能够从大量高度冗余的神经元中提取信息特征，并量化这些神经元编码的信息的复杂性。
It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms’ accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the “Joint Autoencoder”, which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data. The number of neurons that we can simultaneously record from has increased exponentially for decades; today that number is in the thousands of neurons. However, the individual firing rates are highly redundant. One approach to identifying important features from redundant data is to estimate the dimensionality of the neural recordings, which represents the number of degrees of freedom required to describe the data without significant information loss. Better understanding of dimensionality may also uncover the mechanisms of computation within a neural circuit. Circuits carrying out complex computations might be higher-dimensional than those carrying out simpler computations. Typically, studies have quantified neural dimensionality using one of several available methods reflecting a lack of consensus on which method would be most appropriate for neural data. In this work, we used several methods to investigate the accuracy of simulated neural data with properties mimicking those of actual neural recordings. Based on these results, we devised an analysis pipeline to estimate the dimensionality of neural recordings. Our work will allow scientists to extract informative features from a large number of highly redundant neurons, as well as quantify the complexity of information encoded by these neurons.
登录
查看更多内容
神经种群记录中的结构：更简单现象的预期副产品？
影响因子：
25
作者：
Elsayed GF;Cunningham JP
通讯作者：
Cunningham JP
影响因子：
8
作者：
Camastra, F
通讯作者：
Camastra, F
DOI：
10.1016/j.neunet.2009.02.004
发表时间：
2009-11
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
--
作者：
Cunningham JP;Gilja V;Ryu SI;Shenoy KV
通讯作者：
Shenoy KV
影响因子：
25
作者：
Gallego, Juan A.;Perich, Matthew G.;Miller, Lee E.
通讯作者：
Miller, Lee E.
影响因子：
1.8
作者：
Boon, Mei Ying;Henry, Bruce I.;Dain, Stephen J.
通讯作者：
Dain, Stephen J.